Staff Scientist (AI for Self-Driving Labs)

University of Toronto•Toronto, ON

37d

About The Position

The Acceleration Consortium (AC) at the University of Toronto (U of T) is leading a transformative shift in scientific discovery that will accelerate technology development and commercialization. The AC is a global community of academia, industry, and government that leverages the power of artificial intelligence (AI), robotics, materials sciences, and high-throughput chemistry to create self-driving laboratories (SDLs), also called materials acceleration platforms (MAPs). These autonomous labs rapidly design materials and molecules needed for a sustainable, healthy, and resilient future, with applications ranging from renewable energy and consumer electronics to drugs. AC Staff Scientists will advance the field of AI-driven autonomous discovery and develop the materials and molecules required to address society's largest challenges, such as climate change, water pollution, and future pandemics. The Acceleration Consortium (AC) promotes inclusive research environment and supports the EDI priorities of the unit. The Acceleration Consortium received a $200M Canadian First Research Excellence Grant for seven years to develop self-driving labs for chemistry and materials, the largest ever grant to a Canadian University. This grant will provide the Acceleration Consortium with seven years of funding to execute its vision. The AC is developing seven advanced SDLs. These include: SDL0 - A central AI and Automation lab to support all the SDLs SDL1 - Inorganic solid-state compounds for advanced materials and energy SDL2 - Organic small molecules for sustainability and health SDL3 - Medicinal chemistry for improving small molecule drug candidates SDL4 - Polymers for materials science and biological applications SDL5 - Formulations for pharmaceuticals, consumer products, and coatings SDL6 - Biocompatibility with organoids / organ-on-a-chip SDL7 - Synthetic scale-up of materials and molecules (University of British Colombia partnerlab) This posted position is for a Staff Scientist within SDL0: AI & Automation Experience in one or more of the following is desired: - Agentic and sequential decision-making for autonomous experimentation, including active learning and optimal experimental design- Generative and probabilistic modeling, including uncertainty estimation, risk-aware prediction, and data-efficient learning- Continual, transfer, and meta-learning, with emphasis on sim-to-real and real-to-sim generalization- Applied machine learning on real-world experimental or industrial data, including multivariate time-series and noisy, sparse, or incomplete datasets- Close collaboration with experimental scientists, translating scientific objectives into ML-driven or autonomous systems The Staff Scientists will work with a diverse team of leading experts at U of T, including Faculty and Staff Scientists such as: Professor Anatole von Lilienfeld, Kourosh Darvish, Florian Shkurti, Animesh Garg, Alán Aspuru-Guzik, Chris Sutton, Willi Gottstein, Oleksandr Voznyy, and more. The Staff Scientists involved in the AC are highly skilled and experienced researchers who will work independently to develop the AI and automation technologies required to build robust and scalable self-driving labs, manage these SDLs, and design and implement research programs (based on the direction of the AC’s scientific leadership team) that leverage the SDL platforms to discover materials and molecules. Moreover, the Staff Scientists will work collectively, sharing knowledge among each other, faculty, and trainees. This role will report to the Academic Director and Executive Director of the Acceleration Consortium.

Requirements

Education – Ph.D. in Computer Science, Software Engineering, Physics, Chemistry, Materials Science, Biology, or equivalent
Five (5) to 10 years of experience (inclusive of PhD and/or post-graduate work) in research and development, preferably with significant experience in AI for self-driving labs
Experience in AI for science
Experience in the development of AI tools for self-driving labs
Experience working closely with a Principal Investigator or as a Principal Investigator or as Project Director with responsibilities of managing, developing and executing a major research project in the area of AI and automation, including AI utilization in experimental planning, and workflow establishment for seamless integration of experiments and simulations.
Strong experience and expert knowledge of AI and automation
Experience working with industry partners and on industry led research and development projects.
Strong experience presenting research at academic conferences.
Demonstrated record of academic and/or research excellence.
Expert Skills Python, LATEX, Git, Microsoft Office
Strong and effective communicator in oral and written English
Collegial in working with team members and collaborators. Ability to work independently.
Must have a strong publication record.
Demonstrated success in writing and preparing manuscripts, presentations, reports, briefs, and scientific abstracts and manuscripts for peer-reviewed journals.

Nice To Haves

Agentic and sequential decision-making for autonomous experimentation, including active learning and optimal experimental design
Generative and probabilistic modeling, including uncertainty estimation, risk-aware prediction, and data-efficient learning
Continual, transfer, and meta-learning, with emphasis on sim-to-real and real-to-sim generalization
Applied machine learning on real-world experimental or industrial data, including multivariate time-series and noisy, sparse, or incomplete datasets
Close collaboration with experimental scientists, translating scientific objectives into ML-driven or autonomous systems

Responsibilities

SDL and Automation Development
Working with the AC community, including faculty and partners, to determine the required capabilities of the SDLs to be built.
Developing SDL plans to meet user requirements and designing novel instruments for automated material synthesis and characterization.
Developing customized hardware and Python software packages to build SDLs.
Selecting, procurement, and installation of the equipment required for SDLs.
Research Direction
Working independently to develop research programs that leverage the AC’s SDLs and supports the research objectives of AC faculty and industry partners.
Using SDLs to synthesize and characterize large quantities of candidate molecules, calibrating theoretical models with experimental data, predicting promising candidates with computational tools and machine learning algorithms, and elucidating structure-property relationships of emerging molecules, polymers, solid-state materials, formulations, etc.
Managing the research and development projects of AC’s industry partners when implemented in AC labs.
Developing plans supporting research collaborations and estimating financial resources required for programs and/or projects.
Working with Product Managers to ensure research outcomes meet partner requirements.
Promoting AC’s research capacity, including delivering presentations at conferences.
Collaboration in preparing and submitting research proposals to granting agencies and progress reporting.
Preparing manuscripts for submission to peer review publications/journals and stewarding them through the process.
Supporting consulting services related to the application of SDLs for materials discovery for the AC’s partners.
Support research-focused events such as Annual Symposium